Lender, Y. | Search bioRxiv

bioRxiv2023

Spatial and single-cell transcriptomics illuminate bat immunity and barrier tissue evolution

The Egyptian fruit bat displays tolerance to lethal viruses and unique dietary adaptations, but the molecular basis for this is poorly understood. To this end, we generated detailed maps of bat gut, lung and blood cells using spatial and single-cell transcriptomics. We compared bat with mouse and human cells to reveal divergence in genetic programs associated with environmental interactions and immune responses. Complement system genes are transcriptionally divergent, uniquely expressed in bat lung and gut epithelium, and undergo rapid coding-sequence evolution. Specifically in the tip of the gut villus, bat enterocytes express evolutionarily young genes while lacking expression of genes related to specific nutrient absorption. Profiling immune stimulation of PBMCs revealed a monocyte subset with conserved cross-species interferon expression, suggesting strong constraints to avoid an excessive immune response. Our study thus uncovers conserved and divergent immune pathways in bat tissues, providing a unique resource to study bat immunity and evolution.

genomics↗

bioRxiv2023

Immune clustering reveals molecularly distinct subtypes of lung adenocarcinoma

Lung adenocarcinoma, the most prevalent type of non-small cell lung cancer, consists of two driver mutations in KRAS or EGFR. In general, these mutations are mutually exclusive, and biologically and clinically different. In this study, we attempted to find if we could separate lung adenocarcinoma tumors by their immune profile using an unsupervised machine learning method. By projecting RNA-seq data into inferred immune profiles and using unsupervised learning, we were able to divide the lung adenocarcinoma population into three subgroups, one of which appeared to contain mostly EGFR patients. We argue that EGFR mutations in each subgroup are different immunologically which implies a distinct tumor microenvironment and might relate to the relatively high resistance of EGFR-positive tumors to immune checkpoint inhibitors. However, we could not make the same claim about KRAS mutations. Simple SummaryLung adenocarcinoma, the most prevalent type of non-small cell lung cancer, is most commonly driven by mutations in KRAS or EGFR. In this study, we attempted to find if we could separate lung adenocarcinoma tumors by their immune profile using an unsupervised machine learning method. We used established tools to infer the immune profile of each tumor from its RNA-seq and using unsupervised learning, we were able to divide the lung adenocarcinoma population into three subgroups, one of which appeared to contain mostly patients with EGFR mutations. We argue that tumors with EGFR mutations in each subgroup are different immunologically which implies a distinct tumor microenvironment and might relate to the relatively high resistance of EGFR-positive tumors to immune checkpoint inhibitors. However, we could not make the same claim about KRAS mutations.

cancer biology↗